Sentiment analysis based product rating using textual reviews

Sindhu, C.; Vyas, Dyawanapally Veda; Pradyoth, Kommareddy

doi:10.1109/iceca.2017.8212762

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Naive Bayes [1], [8], [10], [11] Naive Bayes is a machine learning algorithm that is written for the classification of probability.…”

Section: Methods Definitionmentioning

confidence: 99%

“…Multiple methods have been applied to classify the text into positive, negative, and neutral based on polarity (See Table 1.). These include Naive Bayes (NB), e.g., [3], [8], [10], and [11], Random Forest (RF) [10], support vector machine (SVM) [10], [11], decision tree (DT) [11], K-Nearest Neighbours algorithm (KNN) [11], Skyttle [12], and SentiText [13]. The study [Hani,20], applies the sentiment scores, which the scores will be generated for the data, then they will be scaled to fit specific characteristics, after that return the text as positive, neutral, or negative.…”

Section: Related Workmentioning

confidence: 99%

“…The analysis of text [16] on the web can also be unsupervised where unorganized groups of data, and by using artificial intelligence, the models are trained in order to understand them in a comprehensive way. Examples include the work of C. Sindhu et al [11] and S. Brody and N. Elhadad [16]. In addition, the study of Y. Chen et al [17] provided an interactive system for visual processing natural language and sentiment lexicon of the reviews using the Natural Language Toolkit (NLTK) library.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews

Alnusyan¹,

Almotairi²,

Almufadhi³

et al. 2022

Int. J. Interact. Mob. Technol.

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Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.

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“…Naive Bayes [1], [8], [10], [11] Naive Bayes is a machine learning algorithm that is written for the classification of probability.…”

Section: Methods Definitionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews

Alnusyan¹,

Almotairi²,

Almufadhi³

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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“…Although earlier studies on SA have focused on the classification of product reviews (Denecke, 2008;Marrese-Taylor et al, 2014;Mullen & Collier, 2004;Pang et al, 2002;Sindhu et al, 2017), SA has only recently received a great interest from the academic community. This section reviews the existing literature to shed light on SA-based ML and its application in the higher education context.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The quality of the final classification of SA is highly dependent on the preparation of the data prior to classification (Ramasubramanian & Ramya, 2013). Thus, it is essential before extracting the subjective features to standardize certain tokens of tweets and avoid the fatal errors that may affect the performance of the ML algorithm (Sindhu et al, 2017). For pre-processing, the Nominal to Text operator was used first to make RapidMiner treat the data as text.…”

Section: Data Pre-processingmentioning

confidence: 99%

Students’ Perceptions of E-Learning in Malaysian Universities: Sentiment Analysis Based Machine Learning Approach

2022

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Aim/Purpose: To gain insight into the opinions and reviews of Malaysian university students regarding e-learning systems, thereby improving the quality and services of these systems and resolving any problems, concerns, and issues that may exist within the institution. Background: This exploratory study examines the students’ perceptions of e-learning in Malaysia based on Sentiment Analysis (SA) to gain a clear insight into their feelings about the quality of e-learning systems and related services in Malaysian universities to determine whether these opinions are positive or negative. Methodology: The data was collected from Twitter; the Full Archive Search API Premium v1.1 tire was chosen to access the tweets from November 1, 2019, to December 30, 2020. The R programming language library package “rtweet” was applied to access the search API and query the tweets. To classify students’ opinions, sentiment analysis-based Machine Learning (ML) with Support Vector Machine (SVM) was utilized. Rapid Miner, a statistical and data mining tool, was used to determine the sentiment of tweets and the accuracy of the ML algorithm. After preparing the data, RapidMiner was used to pre-process and classify the final 1201 tweets based on sentiment, and National Research Council (NRC) word-emotion lexicon was used to detect the presence of eight emotions in the tweets. The confusion matrix is used to determine the classifier’s performance. Contribution: This research provided evidence for the effective use of sentiment analysis as an indicator that may contribute to the development of educational systems, specifically, e-learning systems in Malaysian universities. Findings: Based on the findings, the majority of students have a positive opinion about e-learning systems in Malaysian universities. Precisely, the results showed that 65% of sentiments were classified as positive and 35% as negative. Moreover, among the eight emotions, the majority of the tweets expressed a higher level of trust, anticipation, and joy. Recommendations for Practitioners: The study findings could help classify the teachers’ strengths and weaknesses graphically based on the students’ positive and negative feedback. These findings would also help decision-makers and educationalists be more aware of students’ feelings (sentiments) and concerns. Thus, using social media sentiment analysis should be encouraged as a valuable source of information that may assist their educational decision-making, e-learning development, and performance evaluation. Recommendation for Researchers: The findings may encourage other researchers to apply SA based ML approach and use Twitter as a data source to discover users’ opinions about certain issues in learning and teaching processes. Impact on Society: Our study confirmed that social media data could provide valuable and supportive information about educational systems and procedures in e-learning for appropriate decision-making regarding future development and strategies. Future Research: Future work can experiment with other classification models and different ML classification algorithms as well as other feature extraction methods and compare the results to find the best accuracy that can improve the classification results

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